随机试验中使用有效估计器进行预后调整,以无偏倚地利用历史数据。

IF 1.2 4区 数学
Lauren D Liao, Emilie Højbjerre-Frandsen, Alan E Hubbard, Alejandro Schuler
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引用次数: 0

摘要

虽然随机对照试验(rct)是比较有效性的基础,但由于经济和伦理方面的考虑,它们的样本量通常比观察性研究小得多。因此,有兴趣使用大量的历史数据(无论是观察数据还是先前的试验)来减少试验规模。以前为此目的开发的估计依赖于不切实际的假设,没有这些假设,添加的数据可能会使治疗效果估计产生偏差。最近的工作提出了一种替代方法(预后协变量调整),该方法不施加额外的假设并提高了试验分析的效率。这个想法是使用历史数据来学习预测模型:将结果回归到协变量上。该模型的预测由RCT受试者的基线变量生成,然后用作试验数据线性回归分析中的协变量。在这项工作中,我们将预后调整扩展到使用非参数有效估计器的试验分析,它比线性回归更强大。我们提供的理论解释了为什么预测调整改善了小样本点估计和推断,而没有任何偏差的可能性。模拟证实了这一理论:当试验规模较小时,使用预测调整的有效估计值比不使用预测调整的估计值提供更大的功率(即更小的标准误差)。历史数据和试验数据之间的人口转移会减弱获益,但不会引入偏倚。我们使用诺和诺德公司提供的临床试验数据来展示我们的估计器,该数据评估了2型糖尿病患者的胰岛素治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prognostic adjustment with efficient estimators to unbiasedly leverage historical data in randomized trials.

Although randomized controlled trials (RCTs) are a cornerstone of comparative effectiveness, they typically have much smaller sample size than observational studies due to financial and ethical considerations. Therefore there is interest in using plentiful historical data (either observational data or prior trials) to reduce trial sizes. Previous estimators developed for this purpose rely on unrealistic assumptions, without which the added data can bias the treatment effect estimate. Recent work proposed an alternative method (prognostic covariate adjustment) that imposes no additional assumptions and increases efficiency in trial analyses. The idea is to use historical data to learn a prognostic model: a regression of the outcome onto the covariates. The predictions from this model, generated from the RCT subjects' baseline variables, are then used as a covariate in a linear regression analysis of the trial data. In this work, we extend prognostic adjustment to trial analyses with nonparametric efficient estimators, which are more powerful than linear regression. We provide theory that explains why prognostic adjustment improves small-sample point estimation and inference without any possibility of bias. Simulations corroborate the theory: efficient estimators using prognostic adjustment compared to without provides greater power (i.e., smaller standard errors) when the trial is small. Population shifts between historical and trial data attenuate benefits but do not introduce bias. We showcase our estimator using clinical trial data provided by Novo Nordisk A/S that evaluates insulin therapy for individuals with type 2 diabetes.

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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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